Loading…

Evolutionary computation for architectural design of restaurant layouts

This paper presents the results obtained by NSGA-II and DE on a restaurant layout optimization problem, trying to maximize total profit while minimizing investment. The problem entails the configuration of restaurant functions, the decisions regarding the restaurant shell composition (fraction and p...

Full description

Saved in:

Bibliographic Details
Main Authors:	Ugurlu, Cemre, Chatzikonstantinou, Ioannis, Sariyildiz, Sevil, Tasgetiren, M. Fatih
Format:	Conference Proceeding
Language:	English
Subjects:	architectural design Buildings evolutionary algorithms Genetic algorithms Investment layout design multi-objective optimization Optimization parametric model pareto Sociology Sorting Statistics
Online Access:	Request full text
Tags:	Add Tag No Tags, Be the first to tag this record!

cited_by
cites
container_end_page	2286
container_issue
container_start_page	2279
container_title
container_volume
creator	Ugurlu, Cemre Chatzikonstantinou, Ioannis Sariyildiz, Sevil Tasgetiren, M. Fatih
description	This paper presents the results obtained by NSGA-II and DE on a restaurant layout optimization problem, trying to maximize total profit while minimizing investment. The problem entails the configuration of restaurant functions, the decisions regarding the restaurant shell composition (fraction and position of windows, dimensions), and how to shape and place the kitchen and service areas. The NSGA-II and DE algorithms are implemented in a Parametric Design Environment that is familiar in the architectural practice. We demonstrate that the DE algorithm achieves slightly better performance in terms of hypervolume calculation, and achieve promising results when the Pareto front approximation is examined. To the best of our knowledge, this is the first application of multi-objective approach for restaurant design.
doi_str_mv	10.1109/CEC.2015.7257166
format	conference_proceeding
fullrecord	<record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_7257166</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>7257166</ieee_id><sourcerecordid>7257166</sourcerecordid><originalsourceid>FETCH-LOGICAL-i123t-37b130c42d4dcd8672d36ade8b980637d4c920f4ca6141400f20c6839764f353</originalsourceid><addsrcrecordid>eNotjz1PwzAYhA0CibawI7H4DyS8r-34Y0RRKEiVWDqwVa7tQFAaV7GD1H9PUDvd3TOc7gh5RCgRwTzXTV0ywKpUrFIo5RVZolDGKGGYuiYLNAILACZvZg_aFErpzzuyTOkHAEWFZkHWzW_sp9zFwY4n6uLhOGX7H2kbR2pH993l4PI02p76kLqvgcaWjiFlO7Mh096e4pTTPbltbZ_Cw0VXZPvabOu3YvOxfq9fNkWHjOeCqz1ycIJ54Z3XUjHPpfVB740GyZUXzjBohbMSBQqAloGTmhslRcsrviJP59ouhLA7jt1hnr27_Od_7o5NWg</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Evolutionary computation for architectural design of restaurant layouts</title><source>IEEE Xplore All Conference Series</source><creator>Ugurlu, Cemre ; Chatzikonstantinou, Ioannis ; Sariyildiz, Sevil ; Tasgetiren, M. Fatih</creator><creatorcontrib>Ugurlu, Cemre ; Chatzikonstantinou, Ioannis ; Sariyildiz, Sevil ; Tasgetiren, M. Fatih</creatorcontrib><description>This paper presents the results obtained by NSGA-II and DE on a restaurant layout optimization problem, trying to maximize total profit while minimizing investment. The problem entails the configuration of restaurant functions, the decisions regarding the restaurant shell composition (fraction and position of windows, dimensions), and how to shape and place the kitchen and service areas. The NSGA-II and DE algorithms are implemented in a Parametric Design Environment that is familiar in the architectural practice. We demonstrate that the DE algorithm achieves slightly better performance in terms of hypervolume calculation, and achieve promising results when the Pareto front approximation is examined. To the best of our knowledge, this is the first application of multi-objective approach for restaurant design.</description><identifier>ISSN: 1089-778X</identifier><identifier>EISSN: 1941-0026</identifier><identifier>EISBN: 1479974927</identifier><identifier>EISBN: 9781479974924</identifier><identifier>DOI: 10.1109/CEC.2015.7257166</identifier><language>eng</language><publisher>IEEE</publisher><subject>architectural design ; Buildings ; evolutionary algorithms ; Genetic algorithms ; Investment ; layout design ; multi-objective optimization ; Optimization ; parametric model ; pareto ; Sociology ; Sorting ; Statistics</subject><ispartof>2015 IEEE Congress on Evolutionary Computation (CEC), 2015, p.2279-2286</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7257166$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,23930,23931,25140,27925,54555,54796,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7257166$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Ugurlu, Cemre</creatorcontrib><creatorcontrib>Chatzikonstantinou, Ioannis</creatorcontrib><creatorcontrib>Sariyildiz, Sevil</creatorcontrib><creatorcontrib>Tasgetiren, M. Fatih</creatorcontrib><title>Evolutionary computation for architectural design of restaurant layouts</title><title>2015 IEEE Congress on Evolutionary Computation (CEC)</title><addtitle>CEC</addtitle><description>This paper presents the results obtained by NSGA-II and DE on a restaurant layout optimization problem, trying to maximize total profit while minimizing investment. The problem entails the configuration of restaurant functions, the decisions regarding the restaurant shell composition (fraction and position of windows, dimensions), and how to shape and place the kitchen and service areas. The NSGA-II and DE algorithms are implemented in a Parametric Design Environment that is familiar in the architectural practice. We demonstrate that the DE algorithm achieves slightly better performance in terms of hypervolume calculation, and achieve promising results when the Pareto front approximation is examined. To the best of our knowledge, this is the first application of multi-objective approach for restaurant design.</description><subject>architectural design</subject><subject>Buildings</subject><subject>evolutionary algorithms</subject><subject>Genetic algorithms</subject><subject>Investment</subject><subject>layout design</subject><subject>multi-objective optimization</subject><subject>Optimization</subject><subject>parametric model</subject><subject>pareto</subject><subject>Sociology</subject><subject>Sorting</subject><subject>Statistics</subject><issn>1089-778X</issn><issn>1941-0026</issn><isbn>1479974927</isbn><isbn>9781479974924</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2015</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotjz1PwzAYhA0CibawI7H4DyS8r-34Y0RRKEiVWDqwVa7tQFAaV7GD1H9PUDvd3TOc7gh5RCgRwTzXTV0ywKpUrFIo5RVZolDGKGGYuiYLNAILACZvZg_aFErpzzuyTOkHAEWFZkHWzW_sp9zFwY4n6uLhOGX7H2kbR2pH993l4PI02p76kLqvgcaWjiFlO7Mh096e4pTTPbltbZ_Cw0VXZPvabOu3YvOxfq9fNkWHjOeCqz1ycIJ54Z3XUjHPpfVB740GyZUXzjBohbMSBQqAloGTmhslRcsrviJP59ouhLA7jt1hnr27_Od_7o5NWg</recordid><startdate>20150501</startdate><enddate>20150501</enddate><creator>Ugurlu, Cemre</creator><creator>Chatzikonstantinou, Ioannis</creator><creator>Sariyildiz, Sevil</creator><creator>Tasgetiren, M. Fatih</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20150501</creationdate><title>Evolutionary computation for architectural design of restaurant layouts</title><author>Ugurlu, Cemre ; Chatzikonstantinou, Ioannis ; Sariyildiz, Sevil ; Tasgetiren, M. Fatih</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i123t-37b130c42d4dcd8672d36ade8b980637d4c920f4ca6141400f20c6839764f353</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2015</creationdate><topic>architectural design</topic><topic>Buildings</topic><topic>evolutionary algorithms</topic><topic>Genetic algorithms</topic><topic>Investment</topic><topic>layout design</topic><topic>multi-objective optimization</topic><topic>Optimization</topic><topic>parametric model</topic><topic>pareto</topic><topic>Sociology</topic><topic>Sorting</topic><topic>Statistics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ugurlu, Cemre</creatorcontrib><creatorcontrib>Chatzikonstantinou, Ioannis</creatorcontrib><creatorcontrib>Sariyildiz, Sevil</creatorcontrib><creatorcontrib>Tasgetiren, M. Fatih</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ugurlu, Cemre</au><au>Chatzikonstantinou, Ioannis</au><au>Sariyildiz, Sevil</au><au>Tasgetiren, M. Fatih</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Evolutionary computation for architectural design of restaurant layouts</atitle><btitle>2015 IEEE Congress on Evolutionary Computation (CEC)</btitle><stitle>CEC</stitle><date>2015-05-01</date><risdate>2015</risdate><spage>2279</spage><epage>2286</epage><pages>2279-2286</pages><issn>1089-778X</issn><eissn>1941-0026</eissn><eisbn>1479974927</eisbn><eisbn>9781479974924</eisbn><abstract>This paper presents the results obtained by NSGA-II and DE on a restaurant layout optimization problem, trying to maximize total profit while minimizing investment. The problem entails the configuration of restaurant functions, the decisions regarding the restaurant shell composition (fraction and position of windows, dimensions), and how to shape and place the kitchen and service areas. The NSGA-II and DE algorithms are implemented in a Parametric Design Environment that is familiar in the architectural practice. We demonstrate that the DE algorithm achieves slightly better performance in terms of hypervolume calculation, and achieve promising results when the Pareto front approximation is examined. To the best of our knowledge, this is the first application of multi-objective approach for restaurant design.</abstract><pub>IEEE</pub><doi>10.1109/CEC.2015.7257166</doi><tpages>8</tpages></addata></record>
fulltext	fulltext_linktorsrc
identifier	ISSN: 1089-778X
ispartof	2015 IEEE Congress on Evolutionary Computation (CEC), 2015, p.2279-2286
issn	1089-778X 1941-0026
language	eng
recordid	cdi_ieee_primary_7257166
source	IEEE Xplore All Conference Series
subjects	architectural design Buildings evolutionary algorithms Genetic algorithms Investment layout design multi-objective optimization Optimization parametric model pareto Sociology Sorting Statistics
title	Evolutionary computation for architectural design of restaurant layouts
url	http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T00%3A09%3A12IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Evolutionary%20computation%20for%20architectural%20design%20of%20restaurant%20layouts&rft.btitle=2015%20IEEE%20Congress%20on%20Evolutionary%20Computation%20(CEC)&rft.au=Ugurlu,%20Cemre&rft.date=2015-05-01&rft.spage=2279&rft.epage=2286&rft.pages=2279-2286&rft.issn=1089-778X&rft.eissn=1941-0026&rft_id=info:doi/10.1109/CEC.2015.7257166&rft.eisbn=1479974927&rft.eisbn_list=9781479974924&rft_dat=%3Cieee_CHZPO%3E7257166%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i123t-37b130c42d4dcd8672d36ade8b980637d4c920f4ca6141400f20c6839764f353%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=7257166&rfr_iscdi=true